1. Field of the Invention
The present invention relates to image coding for image signal compression, and more particularly, to an apparatus for image coding using tree-structured quantization based on a wavelet transform.
2. Description of the Related Art
In recent years, research into image coding methods using a wavelet transform has been very active in line with growing demands for wireless image communications related to multimedia applications and widespread Internet use. Here, the wavelet transform means a conversion in which a signal is reconstructed with a plurality of very simple basic functions. That is, the wavelet transform is a method in which data, functions, or operators are decomposed into different frequency components and a resolution and related component corresponding to each scale can be probed. Compared to the Fourier transform, the basic functions of the wavelet transform have a superior time-frequency localization. A process for wavelet-coding an image is formed of removing correlations in an image, quantizing sub-band coefficients, and entropy-coding of quantization coefficients. Since most of the energy is converged into the low-frequency band if an image is transformed into wavelet coefficients, many coefficients in the high-frequency band will have very little energy. Since these coefficients generate small errors, it is efficient to quantize these coefficients into ‘0’ and to indicate their location information using correlations between sub-bands.
Compression methods using the wavelet transform are divided into two groups: one group using a pixel-based zerotree, represented by an embedded zerotree wavelet (EZW) and the other group using a block-based compression method using vector quantization. The EZW uses a zerotree structure for compressing a coefficient map. A zerotree coding method is formed of a sorting process with respect to size, a sorted bit plane transmitting process, and an insignificant coefficient prediction process using similarity between different scales in a wavelet transformed image according to a set partitioning sorting algorithm. The zerotree coding method shows good performance in compression ratio and picture quality and is capable of sequential image restoration. However, the structures of an encoder and decoder for the zerotree coding method are very complicated and computation is relatively complex. Also, the zerotree coding method is sensitive to an environment with much noise because the output bit stream always has a reverse order in the zerotree coding method. If a bit is damaged by noise, it may cause an error in the whole image signal because a vector and coefficients comprising the damaged bit are transmitted with the error.
Meanwhile, the wavelet coding method using vector quantization has two approaches. In the first approach, each wavelet sub-band is divided into block units, and each sub-band is compressed through bit allocation using a different vector quantizer. In the second approach, pixels of the same spatial location in each sub-band are collected and converted into a vector and coded. In the second approach, a vector is generated traversing each sub-band and one codebook is generated during vector quantization. The vector quantization method needs a simpler structure and less computation, and has an error tolerance. However, the coding performance of the vector quantization is poor. The zerotree coding method and the wavelet vector quantization method have their respective merits and drawbacks. Therefore, a new image coding method which maximizes the merits of these methods is needed.